Preferential Structures for Comparative Probabilistic Reasoning
Authors: Matthew Harrison-Trainor, Wesley Holliday, Thomas Icard, III
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we show that a natural modiļ¬cation of the preferential approach yields exactly the same logical system as a probabilistic approach not using single probability measures, but rather sets of probability measures. Thus, the same preferential structures used in the study of non-monotonic logics and belief revision may be used in the study of comparative probabilistic reasoning based on imprecise probabilities. We prove soundness and completeness for the comparative logic of imprecise probabilities (Alon and Heifetz 2014) with respect to a class of preferential models that can be independently motivated. This shows how the two approaches can be seen as fundamentally compatible. The methods we use to prove these results further lead to a complexity result: the logic itself we prove to be NPcomplete. |
| Researcher Affiliation | Academia | Matthew Harrison-Trainor Group in Logic and the Methodology of Science University of California, Berkeley Wesley H. Holliday Department of Philosophy and Group in Logic and the Methodology of Science University of California, Berkeley Thomas F. Icard, III Department of Philosophy and Symbolic Systems Program Stanford University |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training or evaluation. Therefore, no information about public dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, thus no training, validation, or test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and focuses on logical systems and proofs. It does not mention any specific software or programming language dependencies with version numbers required to reproduce its findings. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments, therefore no experimental setup details, such as hyperparameters or training settings, are provided. |